{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "snCCQQR5vx-h"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_total = 8\n",
    "channel = 4\n",
    "epochs = 1000\n",
    "batch_size = 1024\n",
    "x=np.random.randint(0,8,10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ursgArgfwUWR"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GaussianNoise\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "def func():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(msg_total,input_dim=1,activation='relu'))\n",
    "    model.add(Dense(2*channel, activation = 'linear'))\n",
    "    model.add(GaussianNoise(1))\n",
    "    model.add(Dense(msg_total, activation = 'softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['acc'])\n",
    "    return model\n",
    "  \n",
    "estimator=KerasClassifier(build_fn=func,epochs=epochs,batch_size=batch_size,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34017
    },
    "colab_type": "code",
    "id": "qaOD3e-r3GMp",
    "outputId": "e3061f30-3e84-43e3-9fac-8f13b80fb7c8",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "10000/10000 [==============================] - 1s 66us/step - loss: 2.9448 - acc: 0.1303\n",
      "Epoch 2/1000\n",
      "10000/10000 [==============================] - 0s 10us/step - loss: 2.8114 - acc: 0.1289\n",
      "Epoch 3/1000\n",
      "10000/10000 [==============================] - 0s 9us/step - loss: 2.6696 - acc: 0.1367\n",
      "Epoch 4/1000\n",
      "10000/10000 [==============================] - 0s 9us/step - loss: 2.5622 - acc: 0.1434\n",
      "Epoch 5/1000\n",
      "10000/10000 [==============================] - 0s 9us/step - loss: 2.4697 - acc: 0.1399\n",
      "Epoch 6/1000\n",
      "10000/10000 [==============================] - 0s 9us/step - loss: 2.4058 - acc: 0.1426\n",
      "Epoch 7/1000\n",
      "10000/10000 [==============================] - 0s 9us/step - loss: 2.3182 - acc: 0.1498\n",
      "Epoch 8/1000\n",
      "10000/10000 [==============================] - 0s 10us/step - loss: 2.2512 - acc: 0.1479\n",
      "Epoch 9/1000\n",
      "10000/10000 [==============================] - 0s 11us/step - loss: 2.2026 - acc: 0.1444\n",
      "Epoch 10/1000\n",
      "10000/10000 [==============================] - 0s 9us/step - loss: 2.1687 - acc: 0.1496\n",
      "Epoch 11/1000\n",
      "10000/10000 [==============================] - 0s 10us/step - loss: 2.1285 - acc: 0.1680\n",
      "Epoch 12/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 2.0993 - acc: 0.1753\n",
      "Epoch 13/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 2.0944 - acc: 0.1682\n",
      "Epoch 14/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 2.0690 - acc: 0.1777\n",
      "Epoch 15/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 2.0332 - acc: 0.1890\n",
      "Epoch 16/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 2.0151 - acc: 0.1939\n",
      "Epoch 17/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.9993 - acc: 0.1981\n",
      "Epoch 18/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.9789 - acc: 0.2037\n",
      "Epoch 19/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.9697 - acc: 0.2039\n",
      "Epoch 20/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.9414 - acc: 0.2134\n",
      "Epoch 21/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.9284 - acc: 0.2147\n",
      "Epoch 22/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.9102 - acc: 0.2209\n",
      "Epoch 23/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.9036 - acc: 0.2142\n",
      "Epoch 24/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.8875 - acc: 0.2240\n",
      "Epoch 25/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.8679 - acc: 0.2282\n",
      "Epoch 26/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.8491 - acc: 0.2340\n",
      "Epoch 27/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.8258 - acc: 0.2514\n",
      "Epoch 28/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.8281 - acc: 0.2435\n",
      "Epoch 29/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.8133 - acc: 0.2428\n",
      "Epoch 30/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.7907 - acc: 0.2575\n",
      "Epoch 31/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.7932 - acc: 0.2498\n",
      "Epoch 32/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.7649 - acc: 0.2610\n",
      "Epoch 33/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.7588 - acc: 0.2698\n",
      "Epoch 34/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.7533 - acc: 0.2704\n",
      "Epoch 35/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.7288 - acc: 0.2809\n",
      "Epoch 36/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.7234 - acc: 0.2789\n",
      "Epoch 37/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.7106 - acc: 0.2814\n",
      "Epoch 38/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.6928 - acc: 0.2908\n",
      "Epoch 39/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.6888 - acc: 0.2895\n",
      "Epoch 40/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.6724 - acc: 0.2986\n",
      "Epoch 41/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.6653 - acc: 0.2999\n",
      "Epoch 42/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.6482 - acc: 0.3097\n",
      "Epoch 43/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.6396 - acc: 0.3072\n",
      "Epoch 44/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.6244 - acc: 0.3099\n",
      "Epoch 45/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.6197 - acc: 0.3180\n",
      "Epoch 46/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5963 - acc: 0.3300\n",
      "Epoch 47/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5921 - acc: 0.3269\n",
      "Epoch 48/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5723 - acc: 0.3348\n",
      "Epoch 49/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5672 - acc: 0.3368\n",
      "Epoch 50/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5540 - acc: 0.3400\n",
      "Epoch 51/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5415 - acc: 0.3504\n",
      "Epoch 52/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5398 - acc: 0.3406\n",
      "Epoch 53/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.5241 - acc: 0.3592\n",
      "Epoch 54/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5101 - acc: 0.3688\n",
      "Epoch 55/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.5044 - acc: 0.3643\n",
      "Epoch 56/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.4822 - acc: 0.3756\n",
      "Epoch 57/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.4747 - acc: 0.3785\n",
      "Epoch 58/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.4742 - acc: 0.3836\n",
      "Epoch 59/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.4643 - acc: 0.3818\n",
      "Epoch 60/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.4451 - acc: 0.3897\n",
      "Epoch 61/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.4257 - acc: 0.4104\n",
      "Epoch 62/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.4223 - acc: 0.4071\n",
      "Epoch 63/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.4126 - acc: 0.4116\n",
      "Epoch 64/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.4096 - acc: 0.4106\n",
      "Epoch 65/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.3905 - acc: 0.4234\n",
      "Epoch 66/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.3840 - acc: 0.4201\n",
      "Epoch 67/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.3716 - acc: 0.4322\n",
      "Epoch 68/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.3582 - acc: 0.4446\n",
      "Epoch 69/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.3555 - acc: 0.4392\n",
      "Epoch 70/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.3370 - acc: 0.4491\n",
      "Epoch 71/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.3279 - acc: 0.4562\n",
      "Epoch 72/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.3207 - acc: 0.4589\n",
      "Epoch 73/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.3066 - acc: 0.4733\n",
      "Epoch 74/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.2982 - acc: 0.4769\n",
      "Epoch 75/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.2834 - acc: 0.4795\n",
      "Epoch 76/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.2749 - acc: 0.4839\n",
      "Epoch 77/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.2598 - acc: 0.4936\n",
      "Epoch 78/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.2483 - acc: 0.4991\n",
      "Epoch 79/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.2427 - acc: 0.4998\n",
      "Epoch 80/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.2343 - acc: 0.5049\n",
      "Epoch 81/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.2204 - acc: 0.5123\n",
      "Epoch 82/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.2080 - acc: 0.5198\n",
      "Epoch 83/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.2019 - acc: 0.5248\n",
      "Epoch 84/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.1887 - acc: 0.5247\n",
      "Epoch 85/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.1723 - acc: 0.5433\n",
      "Epoch 86/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 1.1684 - acc: 0.5378\n",
      "Epoch 87/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.1469 - acc: 0.5583\n",
      "Epoch 88/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.1419 - acc: 0.5611\n",
      "Epoch 89/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.1357 - acc: 0.5571\n",
      "Epoch 90/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.1281 - acc: 0.5626\n",
      "Epoch 91/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.1068 - acc: 0.5732\n",
      "Epoch 92/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.1015 - acc: 0.5802\n",
      "Epoch 93/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 1.0916 - acc: 0.5856\n",
      "Epoch 94/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.0799 - acc: 0.5899\n",
      "Epoch 95/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.0675 - acc: 0.6000\n",
      "Epoch 96/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.0581 - acc: 0.6024\n",
      "Epoch 97/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.0494 - acc: 0.6070\n",
      "Epoch 98/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.0334 - acc: 0.6129\n",
      "Epoch 99/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.0219 - acc: 0.6273\n",
      "Epoch 100/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 1.0078 - acc: 0.6311\n",
      "Epoch 101/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.9971 - acc: 0.6425\n",
      "Epoch 102/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.9930 - acc: 0.6463\n",
      "Epoch 103/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.9817 - acc: 0.6499\n",
      "Epoch 104/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.9715 - acc: 0.6547\n",
      "Epoch 105/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.9557 - acc: 0.6723\n",
      "Epoch 106/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.9451 - acc: 0.6605\n",
      "Epoch 107/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.9361 - acc: 0.6850\n",
      "Epoch 108/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.9270 - acc: 0.6778\n",
      "Epoch 109/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 0.9134 - acc: 0.6910\n",
      "Epoch 110/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.9032 - acc: 0.6883\n",
      "Epoch 111/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.9010 - acc: 0.6942\n",
      "Epoch 112/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 0.8836 - acc: 0.6967\n",
      "Epoch 113/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.8741 - acc: 0.7171\n",
      "Epoch 114/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 0.8671 - acc: 0.7177\n",
      "Epoch 115/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.8568 - acc: 0.7152\n",
      "Epoch 116/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.8439 - acc: 0.7343\n",
      "Epoch 117/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.8310 - acc: 0.7320\n",
      "Epoch 118/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.8211 - acc: 0.7418\n",
      "Epoch 119/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.8175 - acc: 0.7396\n",
      "Epoch 120/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.8040 - acc: 0.7464\n",
      "Epoch 121/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.7989 - acc: 0.7518\n",
      "Epoch 122/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.7900 - acc: 0.7557\n",
      "Epoch 123/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.7750 - acc: 0.7667\n",
      "Epoch 124/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.7650 - acc: 0.7649\n",
      "Epoch 125/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.7575 - acc: 0.7752\n",
      "Epoch 126/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.7450 - acc: 0.7782\n",
      "Epoch 127/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.7401 - acc: 0.7813\n",
      "Epoch 128/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.7301 - acc: 0.7773\n",
      "Epoch 129/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.7210 - acc: 0.7964\n",
      "Epoch 130/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.7085 - acc: 0.7950\n",
      "Epoch 131/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.7046 - acc: 0.8007\n",
      "Epoch 132/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.6954 - acc: 0.8049\n",
      "Epoch 133/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.6841 - acc: 0.8063\n",
      "Epoch 134/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 0.6756 - acc: 0.8117\n",
      "Epoch 135/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 0.6742 - acc: 0.8169\n",
      "Epoch 136/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.6601 - acc: 0.8223\n",
      "Epoch 137/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.6555 - acc: 0.8221\n",
      "Epoch 138/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.6459 - acc: 0.8231\n",
      "Epoch 139/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.6427 - acc: 0.8239\n",
      "Epoch 140/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.6309 - acc: 0.8401\n",
      "Epoch 141/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.6263 - acc: 0.8299\n",
      "Epoch 142/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.6186 - acc: 0.8353\n",
      "Epoch 143/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.6082 - acc: 0.8443\n",
      "Epoch 144/1000\n",
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      "Epoch 145/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.5925 - acc: 0.8508\n",
      "Epoch 146/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.5859 - acc: 0.8502\n",
      "Epoch 147/1000\n",
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      "Epoch 148/1000\n",
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      "Epoch 149/1000\n",
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      "Epoch 150/1000\n",
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      "Epoch 151/1000\n",
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      "Epoch 152/1000\n",
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      "Epoch 153/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 0.5381 - acc: 0.8771\n",
      "Epoch 154/1000\n",
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      "Epoch 155/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 0.5255 - acc: 0.8764\n",
      "Epoch 156/1000\n",
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    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 864/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 8.6356e-04 - acc: 1.0000\n",
      "Epoch 865/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.9247e-04 - acc: 1.0000\n",
      "Epoch 866/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 8.1799e-04 - acc: 1.0000\n",
      "Epoch 867/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 8.2162e-04 - acc: 1.0000\n",
      "Epoch 868/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.1519e-04 - acc: 1.0000\n",
      "Epoch 869/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.1274e-04 - acc: 1.0000\n",
      "Epoch 870/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.5895e-04 - acc: 0.9999\n",
      "Epoch 871/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.4583e-04 - acc: 1.0000\n",
      "Epoch 872/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.2195e-04 - acc: 1.0000\n",
      "Epoch 873/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.0731e-04 - acc: 1.0000\n",
      "Epoch 874/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.0871e-04 - acc: 1.0000\n",
      "Epoch 875/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.9175e-04 - acc: 1.0000\n",
      "Epoch 876/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.5523e-04 - acc: 1.0000\n",
      "Epoch 877/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.8941e-04 - acc: 1.0000\n",
      "Epoch 878/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.1663e-04 - acc: 1.0000\n",
      "Epoch 879/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.6332e-04 - acc: 1.0000\n",
      "Epoch 880/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.9203e-04 - acc: 1.0000\n",
      "Epoch 881/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 7.8557e-04 - acc: 1.0000\n",
      "Epoch 882/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.0814e-04 - acc: 1.0000\n",
      "Epoch 883/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.5503e-04 - acc: 1.0000\n",
      "Epoch 884/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 7.4491e-04 - acc: 1.0000\n",
      "Epoch 885/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.2770e-04 - acc: 1.0000\n",
      "Epoch 886/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 8.0026e-04 - acc: 1.0000\n",
      "Epoch 887/1000\n",
      "10000/10000 [==============================] - 0s 11us/step - loss: 7.4260e-04 - acc: 1.0000\n",
      "Epoch 888/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 7.2425e-04 - acc: 1.0000\n",
      "Epoch 889/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 7.4050e-04 - acc: 1.0000\n",
      "Epoch 890/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.5353e-04 - acc: 1.0000\n",
      "Epoch 891/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.9769e-04 - acc: 1.0000\n",
      "Epoch 892/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 7.4664e-04 - acc: 1.0000\n",
      "Epoch 893/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.3929e-04 - acc: 1.0000\n",
      "Epoch 894/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.2615e-04 - acc: 1.0000\n",
      "Epoch 895/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 6.3535e-04 - acc: 1.0000\n",
      "Epoch 896/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.9382e-04 - acc: 1.0000\n",
      "Epoch 897/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.7312e-04 - acc: 1.0000\n",
      "Epoch 898/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.5730e-04 - acc: 1.0000\n",
      "Epoch 899/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.3930e-04 - acc: 1.0000\n",
      "Epoch 900/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.1639e-04 - acc: 1.0000\n",
      "Epoch 901/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.2550e-04 - acc: 1.0000\n",
      "Epoch 902/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.8132e-04 - acc: 1.0000\n",
      "Epoch 903/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.5014e-04 - acc: 1.0000\n",
      "Epoch 904/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.5689e-04 - acc: 1.0000\n",
      "Epoch 905/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 6.8253e-04 - acc: 1.0000\n",
      "Epoch 906/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.7193e-04 - acc: 1.0000\n",
      "Epoch 907/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.3112e-04 - acc: 1.0000\n",
      "Epoch 908/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.6056e-04 - acc: 1.0000\n",
      "Epoch 909/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.0685e-04 - acc: 1.0000\n",
      "Epoch 910/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.0324e-04 - acc: 1.0000\n",
      "Epoch 911/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 7.0980e-04 - acc: 0.9999\n",
      "Epoch 912/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.8733e-04 - acc: 1.0000\n",
      "Epoch 913/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 7.9201e-04 - acc: 0.9999\n",
      "Epoch 914/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 7.5426e-04 - acc: 1.0000\n",
      "Epoch 915/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.7570e-04 - acc: 1.0000\n",
      "Epoch 916/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.3874e-04 - acc: 1.0000\n",
      "Epoch 917/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 5.6242e-04 - acc: 1.0000\n",
      "Epoch 918/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.9963e-04 - acc: 1.0000\n",
      "Epoch 919/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.2854e-04 - acc: 1.0000\n",
      "Epoch 920/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 7.3278e-04 - acc: 1.0000\n",
      "Epoch 921/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.1581e-04 - acc: 1.0000\n",
      "Epoch 922/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.4258e-04 - acc: 1.0000\n",
      "Epoch 923/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.0973e-04 - acc: 1.0000\n",
      "Epoch 924/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.6431e-04 - acc: 1.0000\n",
      "Epoch 925/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.0517e-04 - acc: 1.0000\n",
      "Epoch 926/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.6433e-04 - acc: 0.9999\n",
      "Epoch 927/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.9605e-04 - acc: 1.0000\n",
      "Epoch 928/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.4659e-04 - acc: 1.0000\n",
      "Epoch 929/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.2358e-04 - acc: 1.0000\n",
      "Epoch 930/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 6.0844e-04 - acc: 1.0000\n",
      "Epoch 931/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 7.1945e-04 - acc: 0.9999\n",
      "Epoch 932/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.6019e-04 - acc: 1.0000\n",
      "Epoch 933/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.1655e-04 - acc: 1.0000\n",
      "Epoch 934/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.5265e-04 - acc: 1.0000\n",
      "Epoch 935/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.2364e-04 - acc: 1.0000\n",
      "Epoch 936/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.1277e-04 - acc: 1.0000\n",
      "Epoch 937/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.0763e-04 - acc: 1.0000\n",
      "Epoch 938/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.1693e-04 - acc: 1.0000\n",
      "Epoch 939/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.3103e-04 - acc: 1.0000\n",
      "Epoch 940/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.1472e-04 - acc: 1.0000\n",
      "Epoch 941/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.2660e-04 - acc: 1.0000\n",
      "Epoch 942/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.5885e-04 - acc: 1.0000\n",
      "Epoch 943/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.5244e-04 - acc: 0.9999\n",
      "Epoch 944/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.0708e-04 - acc: 1.0000\n",
      "Epoch 945/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.1102e-04 - acc: 1.0000\n",
      "Epoch 946/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 6us/step - loss: 6.1541e-04 - acc: 1.0000\n",
      "Epoch 947/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.1822e-04 - acc: 1.0000\n",
      "Epoch 948/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.0502e-04 - acc: 1.0000\n",
      "Epoch 949/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.1441e-04 - acc: 1.0000\n",
      "Epoch 950/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.5732e-04 - acc: 0.9999\n",
      "Epoch 951/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.7270e-04 - acc: 1.0000\n",
      "Epoch 952/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.8285e-04 - acc: 1.0000\n",
      "Epoch 953/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.8750e-04 - acc: 1.0000\n",
      "Epoch 954/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 6.0997e-04 - acc: 1.0000\n",
      "Epoch 955/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.1992e-04 - acc: 1.0000\n",
      "Epoch 956/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.8657e-04 - acc: 1.0000\n",
      "Epoch 957/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.2028e-04 - acc: 1.0000\n",
      "Epoch 958/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 5.1080e-04 - acc: 1.0000\n",
      "Epoch 959/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.9186e-04 - acc: 1.0000\n",
      "Epoch 960/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.8540e-04 - acc: 1.0000\n",
      "Epoch 961/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.3548e-04 - acc: 1.0000\n",
      "Epoch 962/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.5065e-04 - acc: 1.0000\n",
      "Epoch 963/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.6034e-04 - acc: 1.0000\n",
      "Epoch 964/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.5507e-04 - acc: 1.0000\n",
      "Epoch 965/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 5.4031e-04 - acc: 1.0000\n",
      "Epoch 966/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.8536e-04 - acc: 1.0000\n",
      "Epoch 967/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.3765e-04 - acc: 1.0000\n",
      "Epoch 968/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.4974e-04 - acc: 1.0000\n",
      "Epoch 969/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.2459e-04 - acc: 1.0000\n",
      "Epoch 970/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.3823e-04 - acc: 1.0000\n",
      "Epoch 971/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.0135e-04 - acc: 1.0000\n",
      "Epoch 972/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.2195e-04 - acc: 1.0000\n",
      "Epoch 973/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 4.2514e-04 - acc: 1.0000\n",
      "Epoch 974/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.2084e-04 - acc: 1.0000\n",
      "Epoch 975/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.4070e-04 - acc: 1.0000\n",
      "Epoch 976/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 3.7261e-04 - acc: 1.0000\n",
      "Epoch 977/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.0616e-04 - acc: 1.0000\n",
      "Epoch 978/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.1858e-04 - acc: 1.0000\n",
      "Epoch 979/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.2439e-04 - acc: 1.0000\n",
      "Epoch 980/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.8174e-04 - acc: 1.0000\n",
      "Epoch 981/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.5236e-04 - acc: 1.0000\n",
      "Epoch 982/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.6714e-04 - acc: 1.0000\n",
      "Epoch 983/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.2806e-04 - acc: 1.0000\n",
      "Epoch 984/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.2464e-04 - acc: 1.0000\n",
      "Epoch 985/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 3.8178e-04 - acc: 1.0000\n",
      "Epoch 986/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 3.8933e-04 - acc: 1.0000\n",
      "Epoch 987/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.5417e-04 - acc: 1.0000\n",
      "Epoch 988/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.2199e-04 - acc: 1.0000\n",
      "Epoch 989/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.1795e-04 - acc: 1.0000\n",
      "Epoch 990/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.2314e-04 - acc: 1.0000\n",
      "Epoch 991/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 4.2970e-04 - acc: 1.0000\n",
      "Epoch 992/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 4.1025e-04 - acc: 1.0000\n",
      "Epoch 993/1000\n",
      "10000/10000 [==============================] - 0s 8us/step - loss: 4.0805e-04 - acc: 1.0000\n",
      "Epoch 994/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 3.5179e-04 - acc: 1.0000\n",
      "Epoch 995/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 3.7906e-04 - acc: 1.0000\n",
      "Epoch 996/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 3.8326e-04 - acc: 1.0000\n",
      "Epoch 997/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 3.6553e-04 - acc: 1.0000\n",
      "Epoch 998/1000\n",
      "10000/10000 [==============================] - 0s 7us/step - loss: 3.4998e-04 - acc: 1.0000\n",
      "Epoch 999/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 3.9863e-04 - acc: 1.0000\n",
      "Epoch 1000/1000\n",
      "10000/10000 [==============================] - 0s 6us/step - loss: 3.6468e-04 - acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "history=estimator.fit(x,x)\n",
    "loss_supervised=history.history['loss']\n",
    "acc=history.history['acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "tAQF3qFP41-w",
    "outputId": "4e998912-1000-4ef9-8d31-1c6e4f9f7e67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 4 5 4 4 2 2 0 2 1]\n",
      "10/10 [==============================] - 0s 7ms/step\n",
      "[6 4 5 4 4 2 2 0 2 1]\n"
     ]
    }
   ],
   "source": [
    "x_test=np.random.randint(0,8,10)\n",
    "print(x_test)\n",
    "y_test=estimator.predict(x_test)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XlLTMYqN8y3U"
   },
   "source": [
    "## Alternate learning code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 8)            16          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 8)            72          dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 4, 2)         0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 4, 2)         0           reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 4, 2)         0           reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_2 (Lambda)               ()                   0           lambda_1[0][0]                   \n",
      "                                                                 lambda[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 88\n",
      "Trainable params: 88\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 4, 2)              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 8)                 72        \n",
      "=================================================================\n",
      "Total params: 144\n",
      "Trainable params: 144\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 405us/step - loss: 0.1090\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 6us/step - loss: 0.1087\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 5us/step - loss: 0.1087\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 6us/step - loss: 0.1089\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 6us/step - loss: 0.1088\n",
      "Epoch 1/1\n",
      "1024/1024 [==============================] - 0s 6us/step - loss: 0.1089\n",
      "Epoch 1/1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
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      "Epoch 1/1\n",
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      "Epoch 1/1\n",
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      "Epoch 1/1\n",
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      "Epoch 1/1\n",
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     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import *\n",
    "from sklearn import preprocessing\n",
    "import tensorflow.keras.backend as K\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "Pert_variance = 1e-4\n",
    "\n",
    "def perturbation(x):\n",
    "    w = K.random_normal(shape = (channel,2),\n",
    "    mean=0.0,stddev=Pert_variance**0.5,dtype=None,seed=None)\n",
    "    xp = ((1-Pert_variance)**0.5)*x + w\n",
    "    return xp\n",
    "\n",
    "def loss_tx(y_true, y_pred):\n",
    "    return -y_true*y_pred\n",
    "\n",
    "def get_policy(inp):\n",
    "    xp = inp[0]\n",
    "    x = inp[1]\n",
    "    w = xp - x\n",
    "    policy = -K.sum(w*w)\n",
    "    return policy\n",
    "\n",
    "\n",
    "tx_inp = Input((1,))\n",
    "embbedings_layer = Dense(msg_total, activation = 'relu')(tx_inp)\n",
    "layer_dense = Dense(2*channel, activation = 'relu')(embbedings_layer)\n",
    "to_complex = Reshape((channel,2))(layer_dense)\n",
    "x = Lambda(lambda x: keras.backend.l2_normalize(x))(to_complex)\n",
    "xp = Lambda(perturbation)(to_complex)\n",
    "policy = Lambda(get_policy)([xp,x])\n",
    "\n",
    "model_policy = keras.models.Model(inputs=tx_inp, outputs=policy)\n",
    "model_tx = keras.models.Model(inputs=tx_inp, outputs=xp)\n",
    "\n",
    "model_policy.compile(loss=loss_tx, optimizer=tf.keras.optimizers.SGD(lr = 1e-5))\n",
    "print(model_policy.summary())\n",
    "\n",
    "\n",
    "rx_inp = Input((channel,2))\n",
    "to_flat = Reshape((2*channel,))(rx_inp)\n",
    "fc = Dense(msg_total, activation = 'relu')(to_flat)\n",
    "softmax = Dense(msg_total, activation = 'softmax')(fc)\n",
    "\n",
    "model_rx = keras.models.Model(inputs=rx_inp, outputs=softmax)\n",
    "\n",
    "model_rx.compile(loss='mse', optimizer=tf.keras.optimizers.SGD(lr = 1e-5))\n",
    "print(model_rx.summary())\n",
    "\n",
    "loss_tx = []\n",
    "loss_rx = []\n",
    "for epoch in range(epochs):\n",
    "    raw_input = np.random.randint(0,8,(batch_size))\n",
    "    label = np.zeros((batch_size, 8))\n",
    "    label[np.arange(batch_size), raw_input] = 1\n",
    "    tx_input = raw_input/8.0\n",
    "    xp = model_tx.predict(tx_input)\n",
    "    y = xp + np.random.normal(0,1,(batch_size, channel,2))\n",
    "    pred = model_rx.predict(y)\n",
    "    loss = np.sum(np.square(label - pred), axis = 1)\n",
    "    #   print('epoch: ', epoch)\n",
    "    history_tx = model_policy.fit(tx_input, loss, batch_size=batch_size, epochs=1, verbose=0)\n",
    "    loss_tx.append(history_tx.history['loss'][0])\n",
    "    history_rx = model_rx.fit(xp, label, batch_size=batch_size, epochs=1, verbose=1)\n",
    "    loss_rx.append(history_rx.history['loss'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jbdy4kY4dO4Y6jHKZPHkyiYmJ3HXXXaEO5YyTlZWVf2fU1KlTycjI4O9//3uxcoG+FyKySlVTytpGWF8j8N3ia0cExpgz1TvvvMNjjz1GTk4OrVq1CurCfHlZIjDGeM536siU36hRowLe3lqZwvoagSUCY4wpmyUCY4yJcJ4lAhFpLyJr/brDInKniNQXkcUissXtJ3kVgyUCY4wpm2eJQFU3q2qyqiYDPYFjwH+AicBSVW0HLHXHPWGJwBhjylZVp4YGAt+p6lbgciDVnZ4KXOHVRi0RGOOdt956CxFh06ZN+dP8G1uraBPOFfHSSy+xc+fO/PFbb721xOYeyuPgwYM8++yzFVo2mCamH3roIZYsWVKh9VemqkoE1wC+R/caq2qGO7wLaBxoAREZLyJpIpKWmZlZoY1aIjDGO7Nnz2bYsGElPpVbkUQQqOmFYBRNBC+88AKdOnWq0Lr8lZYIyor13XffpV69eqWWmTJlCoMGDapwfJXF80QgInHAcGBe0XnqPM0W8Ik2VZ2mqimqmtKoUaMKbdsSgTHeyMrKYsWKFTzzzDMBn7gN1ITz0aNHufnmm+nduzfdu3dn/vz5gLMTHz58OBdffDEDBw5k+fLl9O/fnxEjRtChQweuv/56fA++TpkyhV69etGlSxfGjx+PqvL666+TlpbG9ddfT3JyMsePH6d///75rYAmJibywAMPcN5559GnTx92794NwHfffUefPn3o2rUrf/rTn/If2vI3ceJEvvvuu/wmoJcvX84FF1zA8OHD8xPNFVdcQc+ePencuTPTpk3LXzaYJqbHjh2b/96C1q1bM2nSJHr06EHXrl3zj7QyMzMZPHgwnTt35tZbb6VVq1b5TX1XGt9LH7zqcE4FLfIb3ww0cYebAJvLWkfPnj21IjIyVEH1uecqtLgx1dKGDRvyhydMUL3oosrtJkwoO4ZXXnlFx40bp6qq559/vqalpamq6g8//KCdO3dWVdWZM2fq7bffnr/Mfffdpy+//LKqqh44cEDbtWunWVlZOnPmTG3WrJnu27dPVVWXLVumderU0e3bt2tubq726dNH//e//6mq5pdRVR09erQuWLBAVVUvuugi/eKLL/Ln+Y8D+eXuvvtuffjhh1VVddiwYTpr1ixVVX3uuee0Vq1axerpXx9fbDVr1tTvv/8+f5ovpmPHjmnnzp117969qqraqlUrzczM1B9++EGjo6N1zZo1qqp69dVX538OY8aM0Xnz5uWXf/rpp1VV9ZlnntFbbrlFVVVvv/12/ctf/qKqqu+9954CmpmZWSxW/++FD5CmQeynq+LU0LUUnBYCWACMcYfHAPO92rDviODUKa+2YExkmj17NiNHjgRg5MiRJZ4e8rdo0SKmTp1KcnIy/fv3Jzs7m23btgEwePBg6tevn1+2d+/eNG/enKioKJKTk0lPTwdg2bJl/PSnP6Vr16588MEHfP3112VuNy4uLv81lL7mncF509rVV18NlK955969e9OmTZv88aeffjr/aGP79u2FGuTzCdTEdCBXXXVVsTIff/xxfiN7Q4cOJSmp8m+09PTJYhGpBQwG/NuYnQrMFZFbgK3ASK+2b6eGTLgLRSvU+/fv54MPPuCrr75CRMjNzUVE8l8aUxJV5Y033qB9+/aFpq9cubJYY22Bmm3Ozs7mN7/5DWlpabRo0YLJkycXa+o6kNjY2Pwmq0tqAro8/GNdvnw5S5Ys4bPPPqNmzZr5Ca6oYJuY9pWrjDjLw9MjAlU9qqoNVPWQ37R9qjpQVdup6iBV3e/V9i0RGFP5Xn/9dW644Qa2bt1Keno627dvp02bNvmvhvQp2oTzkCFD+Mc//pF/vn/NmjXl2q5vB9uwYUOysrIKvRO4rOaiA+nTp09+U9klNe8cTDPUSUlJ1KxZk02bNrFixYpyxRCMfv36MXfuXMA5qjpw4EClb8OeLDbGlMvs2bO58sorC0375S9/Wez0UNEmnB988EFOnTpFt27d6Ny5Mw8++GC5tluvXj3GjRtHly5dGDJkCL169cqfN3bsWH7961/nXywOxlNPPcUTTzxBt27d+PbbbwM279ygQQP69etHly5dCr0z2Wfo0KHk5OTQsWNHJk6cmP9Gtco0adIkFi1aRJcuXZg3bx5nn302tX1v3aokYd0MdU4OxMbClClQzu+cMdXWmdgMdXV07NgxatSogYjw2muvMXv27Pw7maqTEydOEB0dTUxMDJ999hm33XZbwDesWTPUJfC9F+OhhywRGGMKW7VqFXfccQeqSr169ZgxY0aoQwpo27ZtjBw5kry8POLi4pg+fXqlbyOsE0GRV5oaY0y+Cy64gHXr1oU6jDK1a9eu3NdTyiusrxEYE67OhFO6puqc7vch7BPBmDHQsmWoozCm8iQkJLBv3z5LBgZwksC+fftISEio8DrC+tQQOBeL7a4hE06aN2/Ojh07qGgbXCb8JCQk0Lx58wovH/aJICbGniw24SU2NrbQk63GnK6wPzVkRwTGGFO6sE8EdkRgjDGlC/tEYEcExhhTurBPBHZEYIwxpQv7RBAbC7m5YHfaGWNMYGGfCKzhOWOMKV3YJ4LYWKdvp4eMMSawsE8EdkRgjDGlC/tEYEcExhhTOk8TgYjUE5HXRWSTiGwUkb4iUl9EFovIFrdf+S/g9GNHBMYYUzqvjwj+DixU1Q7AecBGYCKwVFXbAUvdcc/YEYExxpTOs0QgInWBC4EXAVT1pKoeBC4HUt1iqcAVXsUAdkRgjDFl8fKIoA2QCcwUkTUi8oKI1AIaq2qGW2YX0DjQwiIyXkTSRCTtdFpZtERgjDGl8zIRxAA9gOdUtTtwlCKngdRpUD3go16qOk1VU1Q1pVGjRhUOIsqtYV5ehVdhjDFhzctEsAPYoaor3fHXcRLDbhFpAuD293gYQ/57iy0RGGNMYJ4lAlXdBWwXkfbupIHABmABMMadNgaY71UMYEcExhhTFq9fTPNb4FURiQO+B27CST5zReQWYCsw0ssAfIkgN9fLrRhjzJnL00SgqmuBlACzBnq5XX92asgYY0oX9k8W26khY4wpXcQkAjs1ZIwxgUVMIrAjAmOMCSzsE4FdIzDGmNKFfSKwIwJjjCldxCQCu0ZgjDGBhX0isFNDxhhTurBPBHZqyBhjShcxieDRR0EDNm9njDGRLWISwdKlsHNnaGMxxpjqKOwTge8aAcCJE6GLwxhjqquwTwRRfjW0RGCMMcVFVCI4fjx0cRhjTHUV9onA/9TQ0aOhi8MYY6qrsE8E/kcElgiMMaY4SwTGGBPhIioR2DUCY4wpztM3lIlIOnAEyAVyVDVFROoDc4DWQDowUlUPeBWD/zUCe7rYGGOKq4ojggGqmqyqvldWTgSWqmo7YKk77hn/IwJreM4YY4orNRGISLSI/F8lb/NyINUdTgWuqOT1F2KJwBhjSldqIlDVXOD801i/AktEZJWIjHenNVbVDHd4F9A40IIiMl5E0kQkLTMzs8IB+J8askRgjDHFBXONYI2ILADmAfn33ajqm0Ese76q/igiZwGLRWST/0xVVREJ2BScqk4DpgGkpKRUuLk4OyIwxpjSBZMIEoB9wMV+0xQoMxGo6o9uf4+I/AfoDewWkSaqmiEiTYA95Q87eJYIjDGmdGUmAlW9qSIrFpFaQJSqHnGHLwGmAAuAMcBUtz+/IusPliUCY4wpXZl3DYlIcxH5j4jscbs3RKR5EOtuDHwsIuuAz4F3VHUhTgIYLCJbgEHuuGfs9lFjjCldMKeGZgKzgKvd8dHutMGlLaSq3wPnBZi+DxhYvjArzo4IjDGmdME8R9BIVWeqao7bvQQ08jiuSmOJwBhjShdMItgnIqPdZwqiRWQ0zsXjM4IlAmOMKV0wieBmYCTOPf8ZwAigQheQQ6FWrYJhSwTGGFNcqdcIRCQauEpVh1dRPJUuPt65SBwVZYnAGGMCCebJ4murKBbPiDid3TVkjDHFBXPX0Cci8k+cFkP9nyxe7VlUHoiOtiMCY4wJJJhEkOz2p/hNUwo/aVztWSIwxpjAyrpGEAU8p6pzqygez1giMMaYwMq6RpAH3FNFsXjKEoExxgQWzO2jS0TkLhFpISL1fZ3nkVUySwTGGBNYMNcIRrn92/2mKdC28sPxjiUCY4wJLJjWR9tURSBei4qy20eNMSaQYFofrSkifxKRae54OxG5zPvQKpcdERhjTGDBXCOYCZwEfuaO/wg84llEHrFEYIwxgQWTCM5R1b8CpwBU9RggnkblAUsExhgTWDCJ4KSI1MC5QIyInAOc8DQqD1giMMaYwIK5a2gSsBBoISKvAv2AsV4G5QVLBMYYE1gwdw0tFpHVQB+cU0ITVHVvsBtwWzBNA35U1cvcZxDmAK2BdGCkqh6oQOzlEh0NOTleb8UYY848wZwaQlX3qeo7qvp2eZKAawKw0W98IrBUVdsBS91xzyUmwrZtVbElY4w5swSVCCrKfcn9MOAFv8mXA6nucCpwhZcx+KxaBStXwvvvV8XWjDHmzOFpIgCewmmryP9RrsaqmuEO7wIaB1pQRMaLSJqIpGVmZlZaQKvPqMazjTHGe8E8UFY/QBcbxHKXAXtUdVVJZVRVce9GCjBvmqqmqGpKo0aNytpc0Bo0qLRVGWNMWAjmrqHVQAvgAM7F4nrALhHZDYwrZUffDxguIpcCCUAdEXkF2C0iTVQ1Q0SaAHtOuxblUKdOVW7NGGOqv2BODS0GLlXVhqraAPg58DbwG+DZkhZS1ftUtbmqtgauAT5Q1dHAAmCMW2wMMP804g/a2287fWtvyBhjCgsmEfRR1fxLrKq6COirqiuA+ApscyowWES2AIPccc916uT0T56siq0ZY8yZI5hTQxkici/wmjs+Cuf0TjSFLwKXSFWXA8vd4X3AwHJHeppi3asap05V9ZaNMaZ6C+aI4DqgOfCW27V0p0UDI70LrXLFxTl9OyIwxpjCgnmyeC/w2xJmf1u54XjHjgiMMSawMhOBiPwEuAunSYj88qp6sXdhVT47IjDGmMCCuUYwD/gXztPBZ2yzbXZEYIwxgQWTCHJU9TnPI/GYJQJjjAksmIvF/xWR34hIE/+niz2PrJKJQEyMnRoyxpiigjki8D38dbffNAXaVn443oqNtSMCY4wpKpi7htpURSBVIS7OjgiMMaaoEhOBiFysqh+IyFWB5qvqm96F5Y3ERDhyJNRRGGNM9VLaEcFFwAfALwLMU+CMSwRJSXDA83ehGWPMmaXERKCqk9z+TVUXjrcsERhjTHHBPFAWD/yS4g+UTfEuLG8kJUF6eqijMMaY6iWYu4bmA4eAVcAJb8PxVr16cOhQqKMwxpjqJZhE0FxVh3oeSRWIj4cTZ3QqM8aYyhfMA2WfikhXzyOpAvHxdvuoMcYUFcwRwfnAWBH5AefUkOC8bribp5F5IC7OjgiMMaaoYBLBzz2PoorYA2XGGFNciaeGRMT3mvcjJXSlEpEEEflcRNaJyEYRmepOry8ii0Vki9tPOv1qBCcuzmliQrWqtmiMMdVfadcIZrn9VUCa21/lN16WE8DFqnoe0A0YICIXABOBparaDljqjleJePcNy9bekDHGFCgxEajqZW6/jaq2dfu+rswG59SR5Y7G4rza8gBwOZDqTk8FrjitGpRDjHsibMaMqtqiMcZUf8HcNYSIJIlIbxG50NcFuVy0iKwF9gDLVXU90FhVM9wiu4DGJSw7XkTSRCQtMzMzmM2VKTvb6d92W6WszhhjwkIwTxbfCkzAeYH9WqAP8BlQ5qsqVTUXSBaResD7IjKgyHwVkYBn7FV1GjANICUlpVLO6tsdQ8YYU1wwRwQTgF7AVlUdAHQHDpZnI6p6EHgHSAF2i0gTALe/p1wRn4bjx52+SFVt0Rhjqr9gEkG2qmaD0+6Qqm4C2pe1kIg0co8EEJEawGCcI4oFFLzsZgxOExZVwpcIYoK5adYYYyJEMLvEHe4O/S1gsYgcALYGsVwTIFVEonASziuqulhEVgNzReQWdz0jKxh7ufmuEVgiMMaYAqLluKleRC4C6gILVbXKHs1KSUnRtLRg7lgt3c6d0KwZjB4NL79cCYEZY0w1JiKrVDWlrHKl/jYWkWjga1XtAKCPkgJmAAAXQUlEQVSqH1ZSfCHRtKmTCOLiQh2JMcZUH6VeI3Dv+tksIi2rKB7P2QvsjTGmsGDOlicBX4vI58BR30RVHe5ZVB6yRGCMMYUFkwge9DyKKhQbCwcPOncQ1agR6miMMSb0grl99FJV/dC/Ay71OjCvxMbCwoXQvHmoIzHGmOohmEQwOMC0M7Zpat/Txfv3hzYOY4ypLko8NSQitwG/AdqKyJd+s2oDn3gdmFc2bQp1BMYYU72Udo1gFvAe8BiFm4o+oqr2e9oYY8JEiYlAVQ8Bh4Brqy4cY4wxVS2oZqiNMcaEr4hLBGPGlF3GGGMiScQlghEjQh2BMcZULxGXCKydIWOMKSziEkFsbKgjMMaY6iXiEoEdERhjTGERlwjspTTGGFNYxCUCe1+xMcYU5lkiEJEWIrJMRDaIyNciMsGdXl9EFovIFref5FUMxhhjyublEUEO8EdV7QT0AW4XkU44zVUsVdV2wFIKN1/hOf8jAntdpTHGeJgIVDVDVVe7w0eAjUAz4HIg1S2WClzhVQyB+CeCJUuqcsvGGFM9Vck1AhFpDXQHVgKNVTXDnbULaFzCMuNFJE1E0jIzMystljp1/LdRaas1xpgzlueJQEQSgTeAO1X1sP88VVVAAy2nqtNUNUVVUxo1alRp8fzkJ/6xVdpqjTHmjOVpIhCRWJwk8KqqvulO3i0iTdz5TYA9XsYQSO3avviqesvGGFP9eHnXkAAvAhtV9Qm/WQsAX9NvY4D5XsVQkii31pYIjDEmuJfXV1Q/4AbgKxFZ6067H5gKzBWRW4CtwEgPYwjIEoExxhTwLBGo6sdASbvagV5tNxh5eU7/8OHSyxljTCSIuCeLAU6dcvrz5sHmzaGNxRhjQi0iE0GnTgXD27aFLg5jjKkOIjIRvPdewXBWVujiMMaY6iAiE0HDhjBjhjNsicAYE+kiMhEADB3q9I8eDW0cxhgTahGbCBITnb4dERhjIl3EJoKaNZ2+JQJjTKSL2EQQHe28v/i112D79lBHY4wxoROxiQCc5wk2b4aWLUMdiTHGhE5EJwJjjDGWCIwxJuJZIjDGmAhnicAYYyKcJQJjjIlwlgiMMSbCWSJwacA3JxtjTPizROCKirIX1RhjIpOX7yyeISJ7RGS937T6IrJYRLa4/SSvtl8RzzwT6giMMabqeXlE8BIwtMi0icBSVW0HLHXHQ+af/4QmTQrG778fTp4MXTzGGBMKniUCVf0I2F9k8uVAqjucClzh1faDcfvtMG1a4Wnr1wcua4wx4aqqrxE0VtUMd3gX0LikgiIyXkTSRCQtMzPTs4Di4gqPHz/u2aaMMaZaCtnFYlVVoMR7dVR1mqqmqGpKo0aNPIvj4ovhzjth1ixn/MQJzzZljDHVUlUngt0i0gTA7e+p4u0XExMDTz4J55zjjD/4oN1KaoyJLFWdCBYAY9zhMcD8Kt5+iRISnP6nn8JHH8FVV8H+olc4jDEmDHl5++hs4DOgvYjsEJFbgKnAYBHZAgxyx6uF+PiC4VGj4D//cRKCMcaEuxivVqyq15Ywa6BX2zwdviMCgN27i08zxphwZU8WuwLt9O3CsTEmElgicAVKBNnZVR+HMcZUNUsELjsiMMZEKksErrg4aNWq8DRLBMaYSGCJwCUC6emwZw/MnOlMO3ECjh2DCRPgkkugV6+QhmiMMZ4QPQOenkpJSdG0tLQq297hw1C3Lgwe7Jwy+u9/C+adAR+XMcYAICKrVDWlrHKe3T56JvM9U7B4cWjjMMaYqmCnhgIo2hCdMcaEM0sEAYiUPC83t+riMMaYqmCJoAQ/+YnT79Ch8PSsrKqPxRhjvGSJoAQDBjj9224rPP0Pf4A77oBBgwqmHTwIL7xgF5KNMWem8L5YvHq103LcnXeWe9EDB5x+w4bQrx988okzPmNG4XJr1kCPHs5wjx4Fw8YYc6YI7yOC6dPh97+Hv/613D/XH30ULr/c6ZYtc04JtW1buMzWrYV3/OV933F2Nrz7bvmWMcaYyhbeieAf/3DalL73Xujd23kN2fffQ15emYueey689RbUqgWxsU5/9erCZXr3Ljzue83lqlXw2WcF07OzYexYJ3H4u/tuGDbMKV+aQ4dgx44yQz4tX3xR0OpqZfnqK1iwoHLX6ZOVBStWeLPuSPPQQ9CunXfr37XLWf833wSe//LLkJYGO3c6ZU0IqGq173r27KkVduqU6vTpqm3bqjrHBaqxsc74xRer3nST6uTJqjNnqn7wgeqWLapZWSWurmvXgtUU7QYPVn333YLx7GxnmXfeKZg2b17Buvr3d6YtWqT60kuqd9xReFsZGU74bdo45Urz5JOqzz9f8vy8PNXPP3f6ubmqqamqJ04UzAfVVq1K30Z5+eocrF27VH/8Mbiyv/iFs+6DB4vP27tXdd8+1Y0bVffvL3kdJ0+qjhihunp18DFWlU8/DVw3L5T37+TzySfOcmlppZd7+mmn3G23lb79isZR1U6dUr3/ftXt20MdSdmANA1iHxvynXww3WklAp9Tp1RXrnT2lvfeq3rNNap9+qg2aRJ4r16njmrHjqoXXqj6y1+q3n676sMPq06frofmvKcTx+4sMSEU7a65pvD4/v2qEycGLjt5suqtt6r++9/O+M03F/4H2bBBNSdH9csvC6q2fHlBmbw81Xvucdbjb/ZsZ/6cOaovv+wMP/646sKFqikpxf8J335b9euvnXWrqs6dG/xO2se3znvuUf3hB2fajh2B/4HmzCkew44dTjIMpE4dp6xvvTt3qs6f7yQ5/89zyJCCZS67zPlb5OU5yWLoUKdMvXqqx46pvvpq4O19/73qkSOFp2VnO1+J7dudeRddpLpqVZAfjB//ZPzee6o9eqiuX1/8s3jvPedHxpo1TvyqqocOlX97PidPqv73v853ybetkyedBDpihOqePU69t24N/LsoL6/gt9Wf/1y4Pj17qo4e7SSzO+90PiffD6WisrKCTwT79xfUXdX5fTdkiOqmTWV/FlOnqn74ofM92bPHmfb22059S/P6687/2u7dqgcOOH/3RYucOC++2PnelGT8+MJ13rix4MdhILNmqS5YUHo85WWJoDyys50jgSVLnJ/mjz2m+tvfOgngwgtVO3RQTUoKuOdeTydNT0oOOimcTjdtWvEEo6p63nkF0779tmD4hRecf8xjx5xfML5/2kmTSt7GjBnFp3XpUjD86aeqN97o7Ej9nTypum1b4X/UoutZsKDwP/u2bc5Hn5FRuNzevU7ObtnSGV+yxNnZ+o6s/vjHgrL//rfqihUFZYt2Z53lbGvlysKf4/jxgcv/7GdO+Z07ncQwZowzPTnZSTQHDjjz/ZPvsGFOv39/1X/+09l5PPec85l//LEzb/585xe+b0ewe3fBDnLvXtV//StwPOvXq/7tb4WnxcQUJM7p052duarz2W/Y4NT1v/9V/d3vnJ3Yrl1OIr/gAufrvHat6k9/6izft2/g7Z5zTuHxjRudBDF1avHvoe/HyqJFqh99VPp3+J13nL/7vHnOOidMKF7mq69Un3jC+X5PmeLE/+WXBfNvvDHwd3jdOue7dOWVzvIbNqg++6yTLIqW9f3NBgxwEsKDDzr/J9Onqw4aVPCdLrpccrKTAHzjP/2pU/b221X/8Q/nb9mlS+HPaPVq1aNHC3+/tmxxkuX27c5v1EOHCsqvWeNM37bt9Hdt1ToRAEOBzcC3wMSyynueCIJ1/LjzE2nlSuc/+/nnnT3rr3+tBy69TvelXKI3J76mV0X9R3/Ns4W+QF1Zd9qJIFCXkFD+MomJlbPt3/9edfHi4kc3c+c6H01py3bqVPmfRWV1Z59d+vw77ww8vUWL4Na/cGHh8datSy7brVtw62zVKnSfV716Tr9HD+c7Eeq/X3XsiibW0jr//9e33ir846q8qm0iAKKB74C2QBywDuhU2jLVJhEEKy9Ps3cf1E3vfa9fzvhCNz/5jp58+jld8+t/aY2YEwqqj7aZrqB6Xc039Xexz+i/Ga3JrFZQvYu/lvgluSz63Ur9gg7ovFv/MHxLqWVuuvJAyP+RKqMbOzbw9MGDK77OZ5+t2jo0aFBwSivSurp1VePjQx9HVXezZ1d8V1SdE0Ff4H2/8fuA+0pb5oxLBKU4erTgvHtGht85w5Mn9Ye0vfrlf9NVV67U9x5J05UPv687/vqqHpz6nPZvk66gOn3QbE274mF9uNtcndR+tk5qk6oHew3SrG599ara7+tlNZboh/Wv0IcSHteR0fP0AR7W23hG+/CpRnNKQfW3/F1X0Fu30yz/2/Zz3lFQfZT78r+A1/GKZtBYFXQv9fOnj+Q1BdU6HNRrmKVX8obOZpTezAuayGEF1ST26XIu1Je5XhuxW4fzlh6itgq5+etpwVa9iRe1DgcVVBuQqbcwXSfzULF/hjujnlJQ/XP0n/On/T3uLt1bq6Vq7dr6Wo2xCqpptfvry7V+paDaPWadDoz9UEF111ldVZs21YNNOujz9e7R/511Vf56Mpsn66qzL9UL4lfo8BqLtGfcOn2/8Q26udkAXdj4xvxyf036i+a2OUd/X/cF/UXNxfpKozs1p207Xdb0Ov2i2eX55brHrdef11ym65pfqm1jthaqx7WJC7RT3Df54zfWfkNjOaFJUQc0mlP6WIOCHwHrWl6mN9T+j/6/hn/R9NYX6fSz7tecc9vrsXO66PW139IFTcbrjLPuLfZZdY7brDfXmactYn7UrnGb9NY6cxRUFzUbq8+cNVl/Vy9V17cepv9qXPhzXtr8Rs1ql6zPN35Qlza/UZ89a5L2jP9KXzr7Xh1U8xMF1XvrT8svn9rkXn2k4ZN6bmy6Noremz/9d0n/1hG1F+onra7Vz1tfrbfVm6UtY37UO5Je0bvqv6h9azg/eJ5q/Ki+2vQufbfFeD38k575309QndHkfl3V+iqNE+eH08Can2peh46a26GTXpG4OL/cDXXf0nnNJuh58Rv1gQbP6aIWN+urTe/SelEHtW3sVn2r+e35ZZe1vFH/3vhRff7sh7RXwjp9qOEzenb0nvz53eI3FvssG0dnar2og/rrerP14pqf6cIWt+qspn/UD1uNVlC9uvZ7CqovNnlAX2jygC5reaNuO3eAtoz5USckpWq/GmnaOf4b7Rq/SUE1o90FekvdedoiZqcOrvWxNo3ZVWh777UYV2j84UZPac6yjyq8vwk2EVR5M9QiMgIYqqq3uuM3AD9V1TuKlBsPjAdo2bJlz61F772MQN9957w8J6Y8jwGqwqlTzssVsrOdYV+Xk5M/nJt9ityTucRxkoMHlP0HhLYNDxcqe/iIUCfhJJqn7DxQg2b1jjq34vq+t3l5aJ5y4lQUCTE5hee581ElJweiJQ9RZ/zYiWgOHE+gWeKh/LKHs+M4cVKon3CMEznR1Iw5yc5DtWhS6zA/HqlDw4QsEqJOBlw/qpzMiSKWUwiB55OXx8ncaKJEiYnKK/isin52bi8rJ4Fa0dlEiRab75OnUni+/58gL5qDJ2vSKP4wIk7ZNQda0bN+esl/t3LYdawO6w+1YNDZ68su7FcvRdiVXY/aMcepHVvCu1nd8t8caUK7xAyyc2OJj84pVFdVyNFoYiUnqHh3Z9elccKhoMpmHK9Ho/jDBX8n1+f7zyEl6fsSP3Nfm2E5eVFk58WSGFP8TVM5eVFEiXIqL5r4aCf2XBUEyNUoBC223Yo4nhtLdm4cSXFHC8W341h9asWcoGb0CeKichCBQ6dqUCv6BLtP1KVZjQNw//2QnFyh7QbbDHW1TQT+qvp9BMYYEw6CTQSheKDsR6CF33hzd5oxxpgQCEUi+AJoJyJtRCQOuAbw6PlTY4wxZanyRudUNUdE7gDex7mDaIaqfl3VcRhjjHGEpPVRVX0XsObWjDGmGgjvRueMMcaUyRKBMcZEOEsExhgT4SwRGGNMhKvyB8oqQkQygYo+WtwQ2FuJ4ZwJrM6RweocGU6nzq1UtVFZhc6IRHA6RCQtmCfrwonVOTJYnSNDVdTZTg0ZY0yEs0RgjDERLhISwbRQBxACVufIYHWODJ7XOeyvERhjjCldJBwRGGOMKYUlAmOMiXBhmwhEZKiIbBaRb0VkYqjjqSwi0kJElonIBhH5WkQmuNPri8hiEdni9pP8lrnP/Rw2i8iQ0EV/ekQkWkTWiMjb7nhY11lE6onI6yKySUQ2ikjfCKjzfe53e72IzBaRhHCrs4jMEJE9IrLeb1q56ygiPUXkK3fe0yK+d7JVQDDvszzTOpzmrb8D2gJxwDqgU6jjqqS6NQF6uMO1gW+ATsBfgYnu9InA4+5wJ7f+8UAb93OJDnU9Klj3PwCzgLfd8bCuM5AK3OoOxwH1wrnOQGvgB6CGOz4XGBtudQYuBHoA6/2mlbuOwOdAH0CA94CfVzSmcD0i6A18q6rfq+pJ4DXg8hDHVClUNUNVV7vDR4CNQDOc+qW6xVKBK9zhy4HXVPWEqv4AfIvz+ZxRRKQ5MAx4wW9y2NZZROri7DBeBFDVk6p6kDCuM3AYOAXUEJEYoCawkzCrs6p+BOwvMrlcdRSRJkAdVV2hTlb4t98y5RauiaAZsN1vfIc7LayISGugO7ASaKyqGe6sXUBjdzhcPoungHsA/zeJh3Od2wCZwEz3dNgLIlKLMK6zqu4H/g/YBmQAh1R1EWFcZz/lrWMzd7jo9AoJ10QQ9kQkEXgDuFNVD/vPc38hhM19wSJyGbBHVVeVVCbc6ozz0qgewHOq2h04inPKIF+41VlEzgF+j5MEmwK1RGS0f5lwq3MgoahjuCaCH4EWfuPN3WlhQURicZLAq6r6pjt5t3u4iNvf404Ph8+iHzBcRNJxTvNdLCKvEN513gHsUNWV7vjrOIkhnOucAnyqqpmqegp4E/gZ4V1nn/LW8Ud3uOj0CgnXRPAF0E5E2ohIHHANsCDEMVUK986AF4GNqvqE36wFwBh3eAww32/6NSISLyJtgHY4F5nOGKp6n6o2V9XWOH/LD1R1NOFd513AdhFp704aCGwgjOsMbAb6iEhN93s+EOcaWDjX2adcdXRPIx0WkT7uZ3Wj3zLlF+or6B5emb8U546a74AHQh1PJdbrfJzDxi+BtW53KdAAWApsAZYA9f2WecD9HDZzGncWVIcO6E/BXUNhXWcgGUhz/9ZvAUkRUOd7cRLeeuBlnLtlwqrOwGycayCncI78bqlIHXGOoNa78/6J21JERTprYsIYYyJcuJ4aMsYYEyRLBMYYE+EsERhjTISzRGCMMRHOEoExxkQ4SwTGeEBE+vtaSTWmurNEYIwxEc4SgYloIjJaRD4XkbUi8rz7zoMsEXnSfd/DUhFp5JZNFpEVIvKliPzH12a8iJwrIktEZJ2IrHbbzAFI9HufwKu+9uJFZKrb5v6XIvJ/Iaq6MfksEZiIJSIdgVFAP1VNBnKB64FaQJqqdgY+BCa5i/wbuFdVuwFf+U1/FXhGVc/DaRvH14pkd+BOnDbl2wL9RKQBcCXQ2V3PI97W0piyWSIwkWwg0BP4QkTWuuNtcZq6nuOWeQU4330/QD1V/dCdngpcKCK1gWaq+h8AVc1W1WNumc9VdYeq5uE0BdIaOARkAy+KyFWAr6wxIWOJwEQyAVJVNdnt2qvq5ADlKtoOywm/4VwgRlVzcF6e8jpwGbCwgus2ptJYIjCRbCkwQkTOgvz3xrbC+b8Y4Za5DvhYVQ8BB0TkAnf6DcCH6rwlboeIXOGuI15Eapa0Qfc9EnVV9V2ctvfP86JixpRHTKgDMCZUVHWDiPwJWCQiUTitQd6O8xKY3u68PTjXEcBpHvhf7o7+e+Amd/oNwPMiMsVdx9WlbLY2MF9EEnCOSP5QydUyptys9VFjihCRLFVNDHUcxlQVOzVkjDERzo4IjDEmwtkRgTHGRDhLBMYYE+EsERhjTISzRGCMMRHOEoExxkS4/w8cZYnhU7hqfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_arr=range(1,epochs+1)\n",
    "plt.title('Training error with epochs')\n",
    "plt.plot(epoch_arr,loss_supervised,'r',label='Supervised training')\n",
    "plt.plot(epoch_arr,loss_tx,'b',label='Alternating training')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('training error')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "supervised.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
